A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals
An important subfield of brain–computer interface is the classification of motor imagery (MI)
signals where a presumed action, for example, imagining the hands' motions, is mentally …
signals where a presumed action, for example, imagining the hands' motions, is mentally …
Unsupervised feature extraction with autoencoders for EEG based multiclass motor imagery BCI
Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important
component of BCI system that helps motor-disabled people interact with the outside world …
component of BCI system that helps motor-disabled people interact with the outside world …
Functional mapping of the brain for brain–computer interfacing: A review
Brain–computer interfacing has been applied in a range of domains including rehabilitation,
neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the …
neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the …
Decoding EEG rhythms during action observation, motor imagery, and execution for standing and sitting
R Chaisaen, P Autthasan, N Mingchinda… - IEEE sensors …, 2020 - ieeexplore.ieee.org
Event-related desynchronization and synchronization (ERD/S) and movement-related
cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower …
cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower …
[HTML][HTML] Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface
The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based
brain-computer interface (BCI) a dynamic system, thus improving its performance is a …
brain-computer interface (BCI) a dynamic system, thus improving its performance is a …
Active physical practice followed by mental practice using BCI-driven hand exoskeleton: a pilot trial for clinical effectiveness and usability
Appropriately combining mental practice (MP) and physical practice (PP) in a poststroke
rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we …
rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we …
Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network
In recent years, there has been a renewal of interest in brain–computer interface (BCI). One
of the BCI tasks is to classify the EEG motor imagery (MI). A great deal of effort has been …
of the BCI tasks is to classify the EEG motor imagery (MI). A great deal of effort has been …
A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions
Brain-computer interface (BCI) aims to translate human intention into a control output signal.
In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity …
In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity …
Online covariate shift detection-based adaptive brain–computer interface to trigger hand exoskeleton feedback for neuro-rehabilitation
A major issue in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the
intrinsic nonstationarities in the brain waves, which may degrade the performance of the …
intrinsic nonstationarities in the brain waves, which may degrade the performance of the …
Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data
Objective. Magnetoencephalography (MEG) based brain–computer interface (BCI) involves
a large number of sensors allowing better spatiotemporal resolution for assessing brain …
a large number of sensors allowing better spatiotemporal resolution for assessing brain …